Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations9240
Missing cells41039
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 MiB
Average record size in memory1.8 KiB

Variable types

Text1
Numeric6
Categorical16
Boolean14

Alerts

Magazine has constant value "False" Constant
Receive More Updates About Our Courses has constant value "False" Constant
Update me on Supply Chain Content has constant value "False" Constant
Get updates on DM Content has constant value "False" Constant
I agree to pay the amount through cheque has constant value "False" Constant
A free copy of Mastering The Interview is highly overall correlated with City and 2 other fieldsHigh correlation
Asymmetrique Activity Index is highly overall correlated with Asymmetrique Activity Score and 2 other fieldsHigh correlation
Asymmetrique Activity Score is highly overall correlated with Asymmetrique Activity Index and 2 other fieldsHigh correlation
Asymmetrique Profile Index is highly overall correlated with Asymmetrique Profile Score and 2 other fieldsHigh correlation
Asymmetrique Profile Score is highly overall correlated with Asymmetrique Profile Index and 2 other fieldsHigh correlation
City is highly overall correlated with A free copy of Mastering The InterviewHigh correlation
Converted is highly overall correlated with Lead Quality and 1 other fieldsHigh correlation
Do Not Email is highly overall correlated with Last ActivityHigh correlation
Last Activity is highly overall correlated with Do Not Email and 1 other fieldsHigh correlation
Last Notable Activity is highly overall correlated with Last ActivityHigh correlation
Lead Origin is highly overall correlated with A free copy of Mastering The Interview and 1 other fieldsHigh correlation
Lead Profile is highly overall correlated with X Education ForumsHigh correlation
Lead Quality is highly overall correlated with Converted and 2 other fieldsHigh correlation
Lead Source is highly overall correlated with A free copy of Mastering The Interview and 1 other fieldsHigh correlation
Newspaper is highly overall correlated with Asymmetrique Activity Index and 3 other fieldsHigh correlation
Page Views Per Visit is highly overall correlated with Total Time Spent on Website and 1 other fieldsHigh correlation
Tags is highly overall correlated with Converted and 2 other fieldsHigh correlation
Total Time Spent on Website is highly overall correlated with Page Views Per Visit and 1 other fieldsHigh correlation
TotalVisits is highly overall correlated with Page Views Per Visit and 1 other fieldsHigh correlation
What is your current occupation is highly overall correlated with X Education ForumsHigh correlation
What matters most to you in choosing a course is highly overall correlated with X Education ForumsHigh correlation
X Education Forums is highly overall correlated with Asymmetrique Activity Index and 8 other fieldsHigh correlation
Do Not Email is highly imbalanced (60.0%) Imbalance
Do Not Call is highly imbalanced (99.7%) Imbalance
Country is highly imbalanced (92.0%) Imbalance
How did you hear about X Education is highly imbalanced (53.0%) Imbalance
What is your current occupation is highly imbalanced (71.1%) Imbalance
What matters most to you in choosing a course is highly imbalanced (99.6%) Imbalance
Search is highly imbalanced (98.4%) Imbalance
Newspaper Article is highly imbalanced (99.7%) Imbalance
X Education Forums is highly imbalanced (99.8%) Imbalance
Newspaper is highly imbalanced (99.8%) Imbalance
Digital Advertisement is highly imbalanced (99.5%) Imbalance
Through Recommendations is highly imbalanced (99.1%) Imbalance
TotalVisits has 137 (1.5%) missing values Missing
Page Views Per Visit has 137 (1.5%) missing values Missing
Last Activity has 103 (1.1%) missing values Missing
Country has 2461 (26.6%) missing values Missing
Specialization has 1438 (15.6%) missing values Missing
How did you hear about X Education has 2207 (23.9%) missing values Missing
What is your current occupation has 2690 (29.1%) missing values Missing
What matters most to you in choosing a course has 2709 (29.3%) missing values Missing
Tags has 3353 (36.3%) missing values Missing
Lead Quality has 4767 (51.6%) missing values Missing
Lead Profile has 2709 (29.3%) missing values Missing
City has 1420 (15.4%) missing values Missing
Asymmetrique Activity Index has 4218 (45.6%) missing values Missing
Asymmetrique Profile Index has 4218 (45.6%) missing values Missing
Asymmetrique Activity Score has 4218 (45.6%) missing values Missing
Asymmetrique Profile Score has 4218 (45.6%) missing values Missing
Prospect ID has unique values Unique
Lead Number has unique values Unique
TotalVisits has 2189 (23.7%) zeros Zeros
Total Time Spent on Website has 2193 (23.7%) zeros Zeros
Page Views Per Visit has 2189 (23.7%) zeros Zeros

Reproduction

Analysis started2025-07-16 09:55:37.865405
Analysis finished2025-07-16 09:55:50.646309
Duration12.78 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Prospect ID
Text

Unique 

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size839.3 KiB
2025-07-16T15:25:50.955396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters332640
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9240 ?
Unique (%)100.0%

Sample

1st row7927b2df-8bba-4d29-b9a2-b6e0beafe620
2nd row2a272436-5132-4136-86fa-dcc88c88f482
3rd row8cc8c611-a219-4f35-ad23-fdfd2656bd8a
4th row0cc2df48-7cf4-4e39-9de9-19797f9b38cc
5th row3256f628-e534-4826-9d63-4a8b88782852
ValueCountFrequency (%)
d2055a36-b268-43a0-beeb-9a715f6a660d 1
 
< 0.1%
571b5c8e-a5b2-4d57-8574-f2ffb06fdeff 1
 
< 0.1%
7927b2df-8bba-4d29-b9a2-b6e0beafe620 1
 
< 0.1%
2a272436-5132-4136-86fa-dcc88c88f482 1
 
< 0.1%
8cc8c611-a219-4f35-ad23-fdfd2656bd8a 1
 
< 0.1%
0cc2df48-7cf4-4e39-9de9-19797f9b38cc 1
 
< 0.1%
3256f628-e534-4826-9d63-4a8b88782852 1
 
< 0.1%
2058ef08-2858-443e-a01f-a9237db2f5ce 1
 
< 0.1%
9fae7df4-169d-489b-afe4-0f3d752542ed 1
 
< 0.1%
20ef72a2-fb3b-45e0-924e-551c5fa59095 1
 
< 0.1%
Other values (9230) 9230
99.9%
2025-07-16T15:25:51.483309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 36960
 
11.1%
4 26383
 
7.9%
8 19749
 
5.9%
a 19646
 
5.9%
b 19453
 
5.8%
9 19396
 
5.8%
6 17607
 
5.3%
2 17574
 
5.3%
7 17542
 
5.3%
e 17482
 
5.3%
Other values (7) 120848
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 332640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 36960
 
11.1%
4 26383
 
7.9%
8 19749
 
5.9%
a 19646
 
5.9%
b 19453
 
5.8%
9 19396
 
5.8%
6 17607
 
5.3%
2 17574
 
5.3%
7 17542
 
5.3%
e 17482
 
5.3%
Other values (7) 120848
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 332640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 36960
 
11.1%
4 26383
 
7.9%
8 19749
 
5.9%
a 19646
 
5.9%
b 19453
 
5.8%
9 19396
 
5.8%
6 17607
 
5.3%
2 17574
 
5.3%
7 17542
 
5.3%
e 17482
 
5.3%
Other values (7) 120848
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 332640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 36960
 
11.1%
4 26383
 
7.9%
8 19749
 
5.9%
a 19646
 
5.9%
b 19453
 
5.8%
9 19396
 
5.8%
6 17607
 
5.3%
2 17574
 
5.3%
7 17542
 
5.3%
e 17482
 
5.3%
Other values (7) 120848
36.3%

Lead Number
Real number (ℝ)

Unique 

Distinct9240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617188.44
Minimum579533
Maximum660737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2025-07-16T15:25:51.663319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum579533
5-th percentile582869.9
Q1596484.5
median615479
Q3637387.25
95-th percentile655404.05
Maximum660737
Range81204
Interquartile range (IQR)40902.75

Descriptive statistics

Standard deviation23405.996
Coefficient of variation (CV)0.037923581
Kurtosis-1.2063933
Mean617188.44
Median Absolute Deviation (MAD)20413.5
Skewness0.14045109
Sum5.7028211 × 109
Variance5.4784063 × 108
MonotonicityNot monotonic
2025-07-16T15:25:51.872019image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
579533 1
 
< 0.1%
660737 1
 
< 0.1%
660728 1
 
< 0.1%
660727 1
 
< 0.1%
660719 1
 
< 0.1%
660681 1
 
< 0.1%
660680 1
 
< 0.1%
660673 1
 
< 0.1%
660664 1
 
< 0.1%
660624 1
 
< 0.1%
Other values (9230) 9230
99.9%
ValueCountFrequency (%)
579533 1
< 0.1%
579538 1
< 0.1%
579545 1
< 0.1%
579546 1
< 0.1%
579564 1
< 0.1%
579615 1
< 0.1%
579622 1
< 0.1%
579642 1
< 0.1%
579697 1
< 0.1%
579701 1
< 0.1%
ValueCountFrequency (%)
660737 1
< 0.1%
660728 1
< 0.1%
660727 1
< 0.1%
660719 1
< 0.1%
660681 1
< 0.1%
660680 1
< 0.1%
660673 1
< 0.1%
660664 1
< 0.1%
660624 1
< 0.1%
660616 1
< 0.1%

Lead Origin
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size644.4 KiB
Landing Page Submission
4886 
API
3580 
Lead Add Form
718 
Lead Import
 
55
Quick Add Form
 
1

Length

Max length23
Median length23
Mean length14.401623
Min length3

Characters and Unicode

Total characters133071
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAPI
2nd rowAPI
3rd rowLanding Page Submission
4th rowLanding Page Submission
5th rowLanding Page Submission

Common Values

ValueCountFrequency (%)
Landing Page Submission 4886
52.9%
API 3580
38.7%
Lead Add Form 718
 
7.8%
Lead Import 55
 
0.6%
Quick Add Form 1
 
< 0.1%

Length

2025-07-16T15:25:52.083155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:52.266473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
landing 4886
23.8%
page 4886
23.8%
submission 4886
23.8%
api 3580
17.5%
lead 773
 
3.8%
add 719
 
3.5%
form 719
 
3.5%
import 55
 
0.3%
quick 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 14659
11.0%
n 14658
11.0%
11265
 
8.5%
a 10545
 
7.9%
g 9772
 
7.3%
s 9772
 
7.3%
P 8466
 
6.4%
d 7097
 
5.3%
o 5660
 
4.3%
m 5660
 
4.3%
Other values (14) 35517
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 14659
11.0%
n 14658
11.0%
11265
 
8.5%
a 10545
 
7.9%
g 9772
 
7.3%
s 9772
 
7.3%
P 8466
 
6.4%
d 7097
 
5.3%
o 5660
 
4.3%
m 5660
 
4.3%
Other values (14) 35517
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 14659
11.0%
n 14658
11.0%
11265
 
8.5%
a 10545
 
7.9%
g 9772
 
7.3%
s 9772
 
7.3%
P 8466
 
6.4%
d 7097
 
5.3%
o 5660
 
4.3%
m 5660
 
4.3%
Other values (14) 35517
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 14659
11.0%
n 14658
11.0%
11265
 
8.5%
a 10545
 
7.9%
g 9772
 
7.3%
s 9772
 
7.3%
P 8466
 
6.4%
d 7097
 
5.3%
o 5660
 
4.3%
m 5660
 
4.3%
Other values (14) 35517
26.7%

Lead Source
Categorical

High correlation 

Distinct21
Distinct (%)0.2%
Missing36
Missing (%)0.4%
Memory size608.2 KiB
Google
2868 
Direct Traffic
2543 
Olark Chat
1755 
Organic Search
1154 
Reference
534 
Other values (16)
350 

Length

Max length17
Median length16
Mean length10.432095
Min length4

Characters and Unicode

Total characters96017
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rowOlark Chat
2nd rowOrganic Search
3rd rowDirect Traffic
4th rowDirect Traffic
5th rowGoogle

Common Values

ValueCountFrequency (%)
Google 2868
31.0%
Direct Traffic 2543
27.5%
Olark Chat 1755
19.0%
Organic Search 1154
12.5%
Reference 534
 
5.8%
Welingak Website 142
 
1.5%
Referral Sites 125
 
1.4%
Facebook 55
 
0.6%
bing 6
 
0.1%
google 5
 
0.1%
Other values (11) 17
 
0.2%
(Missing) 36
 
0.4%

Length

2025-07-16T15:25:52.480168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
google 2873
19.2%
direct 2543
17.0%
traffic 2543
17.0%
chat 1757
11.8%
olark 1755
11.8%
organic 1154
7.7%
search 1154
7.7%
reference 534
 
3.6%
welingak 142
 
1.0%
website 142
 
1.0%
Other values (19) 333
 
2.2%

Most occurring characters

ValueCountFrequency (%)
r 9938
 
10.4%
e 9584
 
10.0%
a 8699
 
9.1%
c 7995
 
8.3%
i 6666
 
6.9%
o 5863
 
6.1%
f 5745
 
6.0%
5726
 
6.0%
l 4916
 
5.1%
t 4570
 
4.8%
Other values (31) 26315
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 9938
 
10.4%
e 9584
 
10.0%
a 8699
 
9.1%
c 7995
 
8.3%
i 6666
 
6.9%
o 5863
 
6.1%
f 5745
 
6.0%
5726
 
6.0%
l 4916
 
5.1%
t 4570
 
4.8%
Other values (31) 26315
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 9938
 
10.4%
e 9584
 
10.0%
a 8699
 
9.1%
c 7995
 
8.3%
i 6666
 
6.9%
o 5863
 
6.1%
f 5745
 
6.0%
5726
 
6.0%
l 4916
 
5.1%
t 4570
 
4.8%
Other values (31) 26315
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 9938
 
10.4%
e 9584
 
10.0%
a 8699
 
9.1%
c 7995
 
8.3%
i 6666
 
6.9%
o 5863
 
6.1%
f 5745
 
6.0%
5726
 
6.0%
l 4916
 
5.1%
t 4570
 
4.8%
Other values (31) 26315
27.4%

Do Not Email
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
8506 
True
 
734
ValueCountFrequency (%)
False 8506
92.1%
True 734
 
7.9%
2025-07-16T15:25:52.640405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Do Not Call
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9238 
True
 
2
ValueCountFrequency (%)
False 9238
> 99.9%
True 2
 
< 0.1%
2025-07-16T15:25:52.762085image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Converted
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size523.5 KiB
0
5679 
1
3561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Length

2025-07-16T15:25:52.907466image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:53.046652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring characters

ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5679
61.5%
1 3561
38.5%

TotalVisits
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct41
Distinct (%)0.5%
Missing137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.4452378
Minimum0
Maximum251
Zeros2189
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2025-07-16T15:25:53.212211image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum251
Range251
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8548527
Coefficient of variation (CV)1.4091488
Kurtosis853.47871
Mean3.4452378
Median Absolute Deviation (MAD)2
Skewness19.911657
Sum31362
Variance23.569595
MonotonicityNot monotonic
2025-07-16T15:25:53.416853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 2189
23.7%
2 1680
18.2%
3 1306
14.1%
4 1120
12.1%
5 783
 
8.5%
6 466
 
5.0%
1 395
 
4.3%
7 309
 
3.3%
8 224
 
2.4%
9 164
 
1.8%
Other values (31) 467
 
5.1%
(Missing) 137
 
1.5%
ValueCountFrequency (%)
0 2189
23.7%
1 395
 
4.3%
2 1680
18.2%
3 1306
14.1%
4 1120
12.1%
5 783
 
8.5%
6 466
 
5.0%
7 309
 
3.3%
8 224
 
2.4%
9 164
 
1.8%
ValueCountFrequency (%)
251 1
< 0.1%
141 1
< 0.1%
115 1
< 0.1%
74 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
43 1
< 0.1%
42 1
< 0.1%
41 1
< 0.1%
32 1
< 0.1%

Total Time Spent on Website
Real number (ℝ)

High correlation  Zeros 

Distinct1731
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean487.69827
Minimum0
Maximum2272
Zeros2193
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2025-07-16T15:25:53.622088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median248
Q3936
95-th percentile1562
Maximum2272
Range2272
Interquartile range (IQR)924

Descriptive statistics

Standard deviation548.02147
Coefficient of variation (CV)1.1236896
Kurtosis-0.40376973
Mean487.69827
Median Absolute Deviation (MAD)248
Skewness0.95645019
Sum4506332
Variance300327.53
MonotonicityNot monotonic
2025-07-16T15:25:53.825690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2193
 
23.7%
60 19
 
0.2%
74 18
 
0.2%
127 18
 
0.2%
75 18
 
0.2%
234 17
 
0.2%
62 17
 
0.2%
157 17
 
0.2%
87 17
 
0.2%
32 17
 
0.2%
Other values (1721) 6889
74.6%
ValueCountFrequency (%)
0 2193
23.7%
1 7
 
0.1%
2 14
 
0.2%
3 9
 
0.1%
4 10
 
0.1%
5 13
 
0.1%
6 7
 
0.1%
7 8
 
0.1%
8 11
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
2272 1
< 0.1%
2253 1
< 0.1%
2226 1
< 0.1%
2217 1
< 0.1%
2207 1
< 0.1%
2170 1
< 0.1%
2140 1
< 0.1%
2137 1
< 0.1%
2125 1
< 0.1%
2117 1
< 0.1%

Page Views Per Visit
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct114
Distinct (%)1.3%
Missing137
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean2.3628199
Minimum0
Maximum55
Zeros2189
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2025-07-16T15:25:54.008428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum55
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1614178
Coefficient of variation (CV)0.91476194
Kurtosis42.362348
Mean2.3628199
Median Absolute Deviation (MAD)1
Skewness2.8717929
Sum21508.75
Variance4.6717267
MonotonicityNot monotonic
2025-07-16T15:25:54.190107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2189
23.7%
2 1795
19.4%
3 1196
12.9%
4 896
9.7%
1 651
 
7.0%
5 517
 
5.6%
1.5 306
 
3.3%
6 244
 
2.6%
2.5 241
 
2.6%
7 133
 
1.4%
Other values (104) 935
10.1%
(Missing) 137
 
1.5%
ValueCountFrequency (%)
0 2189
23.7%
1 651
 
7.0%
1.14 2
 
< 0.1%
1.17 1
 
< 0.1%
1.19 1
 
< 0.1%
1.2 5
 
0.1%
1.21 1
 
< 0.1%
1.22 2
 
< 0.1%
1.23 2
 
< 0.1%
1.25 23
 
0.2%
ValueCountFrequency (%)
55 1
 
< 0.1%
24 1
 
< 0.1%
16 3
 
< 0.1%
15 4
< 0.1%
14.5 1
 
< 0.1%
14 9
0.1%
13 6
0.1%
12.33 1
 
< 0.1%
12 5
0.1%
11.5 1
 
< 0.1%

Last Activity
Categorical

High correlation  Missing 

Distinct17
Distinct (%)0.2%
Missing103
Missing (%)1.1%
Memory size633.9 KiB
Email Opened
3437 
SMS Sent
2745 
Olark Chat Conversation
973 
Page Visited on Website
640 
Converted to Lead
428 
Other values (12)
914 

Length

Max length28
Median length26
Mean length13.400241
Min length8

Characters and Unicode

Total characters122438
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPage Visited on Website
2nd rowEmail Opened
3rd rowEmail Opened
4th rowUnreachable
5th rowConverted to Lead

Common Values

ValueCountFrequency (%)
Email Opened 3437
37.2%
SMS Sent 2745
29.7%
Olark Chat Conversation 973
 
10.5%
Page Visited on Website 640
 
6.9%
Converted to Lead 428
 
4.6%
Email Bounced 326
 
3.5%
Email Link Clicked 267
 
2.9%
Form Submitted on Website 116
 
1.3%
Unreachable 93
 
1.0%
Unsubscribed 61
 
0.7%
Other values (7) 51
 
0.6%
(Missing) 103
 
1.1%

Length

2025-07-16T15:25:54.426444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
email 4034
18.9%
opened 3437
16.1%
sms 2745
12.8%
sent 2745
12.8%
conversation 1003
 
4.7%
olark 973
 
4.6%
chat 973
 
4.6%
on 756
 
3.5%
website 756
 
3.5%
visited 641
 
3.0%
Other values (26) 3320
15.5%

Most occurring characters

ValueCountFrequency (%)
e 15724
12.8%
12246
 
10.0%
n 10171
 
8.3%
S 8353
 
6.8%
a 8312
 
6.8%
i 7815
 
6.4%
t 7217
 
5.9%
d 5755
 
4.7%
l 5380
 
4.4%
O 4410
 
3.6%
Other values (28) 37055
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15724
12.8%
12246
 
10.0%
n 10171
 
8.3%
S 8353
 
6.8%
a 8312
 
6.8%
i 7815
 
6.4%
t 7217
 
5.9%
d 5755
 
4.7%
l 5380
 
4.4%
O 4410
 
3.6%
Other values (28) 37055
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15724
12.8%
12246
 
10.0%
n 10171
 
8.3%
S 8353
 
6.8%
a 8312
 
6.8%
i 7815
 
6.4%
t 7217
 
5.9%
d 5755
 
4.7%
l 5380
 
4.4%
O 4410
 
3.6%
Other values (28) 37055
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15724
12.8%
12246
 
10.0%
n 10171
 
8.3%
S 8353
 
6.8%
a 8312
 
6.8%
i 7815
 
6.4%
t 7217
 
5.9%
d 5755
 
4.7%
l 5380
 
4.4%
O 4410
 
3.6%
Other values (28) 37055
30.3%

Country
Categorical

Imbalance  Missing 

Distinct38
Distinct (%)0.6%
Missing2461
Missing (%)26.6%
Memory size547.1 KiB
India
6492 
United States
 
69
United Arab Emirates
 
53
Singapore
 
24
Saudi Arabia
 
21
Other values (33)
 
120

Length

Max length20
Median length5
Mean length5.291931
Min length4

Characters and Unicode

Total characters35874
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
India 6492
70.3%
United States 69
 
0.7%
United Arab Emirates 53
 
0.6%
Singapore 24
 
0.3%
Saudi Arabia 21
 
0.2%
United Kingdom 15
 
0.2%
Australia 13
 
0.1%
Qatar 10
 
0.1%
Hong Kong 7
 
0.1%
Bahrain 7
 
0.1%
Other values (28) 68
 
0.7%
(Missing) 2461
 
26.6%

Length

2025-07-16T15:25:54.668811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india 6492
92.7%
united 137
 
2.0%
states 69
 
1.0%
arab 53
 
0.8%
emirates 53
 
0.8%
singapore 24
 
0.3%
saudi 21
 
0.3%
arabia 21
 
0.3%
kingdom 15
 
0.2%
australia 13
 
0.2%
Other values (35) 106
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 6891
19.2%
i 6826
19.0%
n 6750
18.8%
d 6680
18.6%
I 6495
18.1%
t 365
 
1.0%
e 321
 
0.9%
225
 
0.6%
r 205
 
0.6%
s 147
 
0.4%
Other values (35) 969
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6891
19.2%
i 6826
19.0%
n 6750
18.8%
d 6680
18.6%
I 6495
18.1%
t 365
 
1.0%
e 321
 
0.9%
225
 
0.6%
r 205
 
0.6%
s 147
 
0.4%
Other values (35) 969
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6891
19.2%
i 6826
19.0%
n 6750
18.8%
d 6680
18.6%
I 6495
18.1%
t 365
 
1.0%
e 321
 
0.9%
225
 
0.6%
r 205
 
0.6%
s 147
 
0.4%
Other values (35) 969
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6891
19.2%
i 6826
19.0%
n 6750
18.8%
d 6680
18.6%
I 6495
18.1%
t 365
 
1.0%
e 321
 
0.9%
225
 
0.6%
r 205
 
0.6%
s 147
 
0.4%
Other values (35) 969
 
2.7%

Specialization
Categorical

Missing 

Distinct19
Distinct (%)0.2%
Missing1438
Missing (%)15.6%
Memory size647.5 KiB
Select
1942 
Finance Management
976 
Human Resource Management
848 
Marketing Management
838 
Operations Management
503 
Other values (14)
2695 

Length

Max length33
Median length23
Mean length17.646885
Min length6

Characters and Unicode

Total characters137681
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowBusiness Administration
4th rowMedia and Advertising
5th rowSelect

Common Values

ValueCountFrequency (%)
Select 1942
21.0%
Finance Management 976
10.6%
Human Resource Management 848
9.2%
Marketing Management 838
9.1%
Operations Management 503
 
5.4%
Business Administration 403
 
4.4%
IT Projects Management 366
 
4.0%
Supply Chain Management 349
 
3.8%
Banking, Investment And Insurance 338
 
3.7%
Media and Advertising 203
 
2.2%
Other values (9) 1036
11.2%
(Missing) 1438
15.6%

Length

2025-07-16T15:25:54.865682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
management 4253
26.2%
select 1942
12.0%
finance 976
 
6.0%
human 848
 
5.2%
resource 848
 
5.2%
marketing 838
 
5.2%
and 817
 
5.0%
business 581
 
3.6%
operations 503
 
3.1%
administration 403
 
2.5%
Other values (21) 4202
25.9%

Most occurring characters

ValueCountFrequency (%)
e 19899
14.5%
n 18135
13.2%
a 14945
10.9%
t 10430
 
7.6%
8409
 
6.1%
i 6355
 
4.6%
m 6045
 
4.4%
g 5705
 
4.1%
M 5518
 
4.0%
s 5489
 
4.0%
Other values (28) 36751
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19899
14.5%
n 18135
13.2%
a 14945
10.9%
t 10430
 
7.6%
8409
 
6.1%
i 6355
 
4.6%
m 6045
 
4.4%
g 5705
 
4.1%
M 5518
 
4.0%
s 5489
 
4.0%
Other values (28) 36751
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19899
14.5%
n 18135
13.2%
a 14945
10.9%
t 10430
 
7.6%
8409
 
6.1%
i 6355
 
4.6%
m 6045
 
4.4%
g 5705
 
4.1%
M 5518
 
4.0%
s 5489
 
4.0%
Other values (28) 36751
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19899
14.5%
n 18135
13.2%
a 14945
10.9%
t 10430
 
7.6%
8409
 
6.1%
i 6355
 
4.6%
m 6045
 
4.4%
g 5705
 
4.1%
M 5518
 
4.0%
s 5489
 
4.0%
Other values (28) 36751
26.7%

How did you hear about X Education
Categorical

Imbalance  Missing 

Distinct10
Distinct (%)0.1%
Missing2207
Missing (%)23.9%
Memory size568.1 KiB
Select
5043 
Online Search
808 
Word Of Mouth
 
348
Student of SomeSchool
 
310
Other
 
186
Other values (5)
 
338

Length

Max length21
Median length6
Mean length8.1246979
Min length3

Characters and Unicode

Total characters57141
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowSelect
4th rowWord Of Mouth
5th rowOther

Common Values

ValueCountFrequency (%)
Select 5043
54.6%
Online Search 808
 
8.7%
Word Of Mouth 348
 
3.8%
Student of SomeSchool 310
 
3.4%
Other 186
 
2.0%
Multiple Sources 152
 
1.6%
Advertisements 70
 
0.8%
Social Media 67
 
0.7%
Email 26
 
0.3%
SMS 23
 
0.2%
(Missing) 2207
23.9%

Length

2025-07-16T15:25:55.048536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:55.246729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
select 5043
53.8%
online 808
 
8.6%
search 808
 
8.6%
of 658
 
7.0%
word 348
 
3.7%
mouth 348
 
3.7%
student 310
 
3.3%
someschool 310
 
3.3%
other 186
 
2.0%
multiple 152
 
1.6%
Other values (6) 405
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 13089
22.9%
S 7046
12.3%
l 6558
11.5%
t 6489
11.4%
c 6380
11.2%
2343
 
4.1%
o 2155
 
3.8%
n 1996
 
3.5%
h 1652
 
2.9%
r 1564
 
2.7%
Other values (14) 7869
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13089
22.9%
S 7046
12.3%
l 6558
11.5%
t 6489
11.4%
c 6380
11.2%
2343
 
4.1%
o 2155
 
3.8%
n 1996
 
3.5%
h 1652
 
2.9%
r 1564
 
2.7%
Other values (14) 7869
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13089
22.9%
S 7046
12.3%
l 6558
11.5%
t 6489
11.4%
c 6380
11.2%
2343
 
4.1%
o 2155
 
3.8%
n 1996
 
3.5%
h 1652
 
2.9%
r 1564
 
2.7%
Other values (14) 7869
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13089
22.9%
S 7046
12.3%
l 6558
11.5%
t 6489
11.4%
c 6380
11.2%
2343
 
4.1%
o 2155
 
3.8%
n 1996
 
3.5%
h 1652
 
2.9%
r 1564
 
2.7%
Other values (14) 7869
13.8%

What is your current occupation
Categorical

High correlation  Imbalance  Missing 

Distinct6
Distinct (%)0.1%
Missing2690
Missing (%)29.1%
Memory size582.0 KiB
Unemployed
5600 
Working Professional
706 
Student
 
210
Other
 
16
Housewife
 
10

Length

Max length20
Median length10
Mean length10.96916
Min length5

Characters and Unicode

Total characters71848
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnemployed
2nd rowUnemployed
3rd rowStudent
4th rowUnemployed
5th rowUnemployed

Common Values

ValueCountFrequency (%)
Unemployed 5600
60.6%
Working Professional 706
 
7.6%
Student 210
 
2.3%
Other 16
 
0.2%
Housewife 10
 
0.1%
Businessman 8
 
0.1%
(Missing) 2690
29.1%

Length

2025-07-16T15:25:55.466358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:55.789104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unemployed 5600
77.2%
working 706
 
9.7%
professional 706
 
9.7%
student 210
 
2.9%
other 16
 
0.2%
housewife 10
 
0.1%
businessman 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 12160
16.9%
o 7728
10.8%
n 7238
10.1%
l 6306
8.8%
d 5810
8.1%
m 5608
7.8%
U 5600
7.8%
p 5600
7.8%
y 5600
7.8%
s 1446
 
2.0%
Other values (17) 8752
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71848
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12160
16.9%
o 7728
10.8%
n 7238
10.1%
l 6306
8.8%
d 5810
8.1%
m 5608
7.8%
U 5600
7.8%
p 5600
7.8%
y 5600
7.8%
s 1446
 
2.0%
Other values (17) 8752
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71848
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12160
16.9%
o 7728
10.8%
n 7238
10.1%
l 6306
8.8%
d 5810
8.1%
m 5608
7.8%
U 5600
7.8%
p 5600
7.8%
y 5600
7.8%
s 1446
 
2.0%
Other values (17) 8752
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71848
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12160
16.9%
o 7728
10.8%
n 7238
10.1%
l 6306
8.8%
d 5810
8.1%
m 5608
7.8%
U 5600
7.8%
p 5600
7.8%
y 5600
7.8%
s 1446
 
2.0%
Other values (17) 8752
12.2%

What matters most to you in choosing a course
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing2709
Missing (%)29.3%
Memory size658.5 KiB
Better Career Prospects
6528 
Flexibility & Convenience
 
2
Other
 
1

Length

Max length25
Median length23
Mean length22.997856
Min length5

Characters and Unicode

Total characters150199
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowBetter Career Prospects
2nd rowBetter Career Prospects
3rd rowBetter Career Prospects
4th rowBetter Career Prospects
5th rowBetter Career Prospects

Common Values

ValueCountFrequency (%)
Better Career Prospects 6528
70.6%
Flexibility & Convenience 2
 
< 0.1%
Other 1
 
< 0.1%
(Missing) 2709
29.3%

Length

2025-07-16T15:25:55.979533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:56.125561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
better 6528
33.3%
career 6528
33.3%
prospects 6528
33.3%
flexibility 2
 
< 0.1%
2
 
< 0.1%
convenience 2
 
< 0.1%
other 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 32649
21.7%
r 26113
17.4%
t 19587
13.0%
13060
8.7%
s 13056
 
8.7%
o 6530
 
4.3%
C 6530
 
4.3%
c 6530
 
4.3%
B 6528
 
4.3%
a 6528
 
4.3%
Other values (13) 13088
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 32649
21.7%
r 26113
17.4%
t 19587
13.0%
13060
8.7%
s 13056
 
8.7%
o 6530
 
4.3%
C 6530
 
4.3%
c 6530
 
4.3%
B 6528
 
4.3%
a 6528
 
4.3%
Other values (13) 13088
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 32649
21.7%
r 26113
17.4%
t 19587
13.0%
13060
8.7%
s 13056
 
8.7%
o 6530
 
4.3%
C 6530
 
4.3%
c 6530
 
4.3%
B 6528
 
4.3%
a 6528
 
4.3%
Other values (13) 13088
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 32649
21.7%
r 26113
17.4%
t 19587
13.0%
13060
8.7%
s 13056
 
8.7%
o 6530
 
4.3%
C 6530
 
4.3%
c 6530
 
4.3%
B 6528
 
4.3%
a 6528
 
4.3%
Other values (13) 13088
8.7%

Search
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9226 
True
 
14
ValueCountFrequency (%)
False 9226
99.8%
True 14
 
0.2%
2025-07-16T15:25:56.234728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Magazine
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2025-07-16T15:25:56.329338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Newspaper Article
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9238 
True
 
2
ValueCountFrequency (%)
False 9238
> 99.9%
True 2
 
< 0.1%
2025-07-16T15:25:56.439171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

X Education Forums
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9239 
True
 
1
ValueCountFrequency (%)
False 9239
> 99.9%
True 1
 
< 0.1%
2025-07-16T15:25:56.553301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Newspaper
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9239 
True
 
1
ValueCountFrequency (%)
False 9239
> 99.9%
True 1
 
< 0.1%
2025-07-16T15:25:56.670299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Digital Advertisement
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9236 
True
 
4
ValueCountFrequency (%)
False 9236
> 99.9%
True 4
 
< 0.1%
2025-07-16T15:25:56.775428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Through Recommendations
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9233 
True
 
7
ValueCountFrequency (%)
False 9233
99.9%
True 7
 
0.1%
2025-07-16T15:25:56.889449image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2025-07-16T15:25:56.989474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Tags
Categorical

High correlation  Missing 

Distinct26
Distinct (%)0.4%
Missing3353
Missing (%)36.3%
Memory size638.5 KiB
Will revert after reading the email
2072 
Ringing
1203 
Interested in other courses
513 
Already a student
465 
Closed by Horizzon
358 
Other values (21)
1276 

Length

Max length49
Median length37
Mean length22.145405
Min length4

Characters and Unicode

Total characters130370
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowInterested in other courses
2nd rowRinging
3rd rowWill revert after reading the email
4th rowRinging
5th rowWill revert after reading the email

Common Values

ValueCountFrequency (%)
Will revert after reading the email 2072
22.4%
Ringing 1203
 
13.0%
Interested in other courses 513
 
5.6%
Already a student 465
 
5.0%
Closed by Horizzon 358
 
3.9%
switched off 240
 
2.6%
Busy 186
 
2.0%
Lost to EINS 175
 
1.9%
Not doing further education 145
 
1.6%
Interested in full time MBA 117
 
1.3%
Other values (16) 413
 
4.5%
(Missing) 3353
36.3%

Length

2025-07-16T15:25:57.130291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 2074
 
9.5%
revert 2072
 
9.5%
will 2072
 
9.5%
after 2072
 
9.5%
reading 2072
 
9.5%
email 2072
 
9.5%
ringing 1203
 
5.5%
in 765
 
3.5%
interested 635
 
2.9%
other 513
 
2.4%
Other values (61) 6215
28.6%

Most occurring characters

ValueCountFrequency (%)
e 17825
13.7%
15995
12.3%
r 11725
 
9.0%
i 11002
 
8.4%
t 10394
 
8.0%
a 7750
 
5.9%
l 7640
 
5.9%
n 7559
 
5.8%
g 4935
 
3.8%
d 4847
 
3.7%
Other values (35) 30698
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17825
13.7%
15995
12.3%
r 11725
 
9.0%
i 11002
 
8.4%
t 10394
 
8.0%
a 7750
 
5.9%
l 7640
 
5.9%
n 7559
 
5.8%
g 4935
 
3.8%
d 4847
 
3.7%
Other values (35) 30698
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17825
13.7%
15995
12.3%
r 11725
 
9.0%
i 11002
 
8.4%
t 10394
 
8.0%
a 7750
 
5.9%
l 7640
 
5.9%
n 7559
 
5.8%
g 4935
 
3.8%
d 4847
 
3.7%
Other values (35) 30698
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17825
13.7%
15995
12.3%
r 11725
 
9.0%
i 11002
 
8.4%
t 10394
 
8.0%
a 7750
 
5.9%
l 7640
 
5.9%
n 7559
 
5.8%
g 4935
 
3.8%
d 4847
 
3.7%
Other values (35) 30698
23.5%

Lead Quality
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.1%
Missing4767
Missing (%)51.6%
Memory size553.1 KiB
Might be
1560 
Not Sure
1092 
High in Relevance
637 
Worst
601 
Low in Relevance
583 

Length

Max length17
Median length8
Mean length9.9213056
Min length5

Characters and Unicode

Total characters44378
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow in Relevance
2nd rowMight be
3rd rowNot Sure
4th rowMight be
5th rowLow in Relevance

Common Values

ValueCountFrequency (%)
Might be 1560
 
16.9%
Not Sure 1092
 
11.8%
High in Relevance 637
 
6.9%
Worst 601
 
6.5%
Low in Relevance 583
 
6.3%
(Missing) 4767
51.6%

Length

2025-07-16T15:25:57.307463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:57.471251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
might 1560
16.3%
be 1560
16.3%
in 1220
12.8%
relevance 1220
12.8%
not 1092
11.4%
sure 1092
11.4%
high 637
6.7%
worst 601
 
6.3%
low 583
 
6.1%

Most occurring characters

ValueCountFrequency (%)
e 6312
14.2%
5092
 
11.5%
i 3417
 
7.7%
t 3253
 
7.3%
n 2440
 
5.5%
o 2276
 
5.1%
g 2197
 
5.0%
h 2197
 
5.0%
r 1693
 
3.8%
M 1560
 
3.5%
Other values (14) 13941
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6312
14.2%
5092
 
11.5%
i 3417
 
7.7%
t 3253
 
7.3%
n 2440
 
5.5%
o 2276
 
5.1%
g 2197
 
5.0%
h 2197
 
5.0%
r 1693
 
3.8%
M 1560
 
3.5%
Other values (14) 13941
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6312
14.2%
5092
 
11.5%
i 3417
 
7.7%
t 3253
 
7.3%
n 2440
 
5.5%
o 2276
 
5.1%
g 2197
 
5.0%
h 2197
 
5.0%
r 1693
 
3.8%
M 1560
 
3.5%
Other values (14) 13941
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6312
14.2%
5092
 
11.5%
i 3417
 
7.7%
t 3253
 
7.3%
n 2440
 
5.5%
o 2276
 
5.1%
g 2197
 
5.0%
h 2197
 
5.0%
r 1693
 
3.8%
M 1560
 
3.5%
Other values (14) 13941
31.4%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2025-07-16T15:25:57.621770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Get updates on DM Content
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2025-07-16T15:25:57.707386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Lead Profile
Categorical

High correlation  Missing 

Distinct6
Distinct (%)0.1%
Missing2709
Missing (%)29.3%
Memory size569.2 KiB
Select
4146 
Potential Lead
1613 
Other Leads
487 
Student of SomeSchool
 
241
Lateral Student
 
24

Length

Max length27
Median length6
Mean length8.9995407
Min length6

Characters and Unicode

Total characters58776
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowPotential Lead
4th rowSelect
5th rowSelect

Common Values

ValueCountFrequency (%)
Select 4146
44.9%
Potential Lead 1613
 
17.5%
Other Leads 487
 
5.3%
Student of SomeSchool 241
 
2.6%
Lateral Student 24
 
0.3%
Dual Specialization Student 20
 
0.2%
(Missing) 2709
29.3%

Length

2025-07-16T15:25:57.847820image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:57.994279image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
select 4146
45.2%
potential 1613
 
17.6%
lead 1613
 
17.6%
other 487
 
5.3%
leads 487
 
5.3%
student 285
 
3.1%
of 241
 
2.6%
someschool 241
 
2.6%
lateral 24
 
0.3%
dual 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 13062
22.2%
t 8473
14.4%
l 6064
10.3%
S 4933
 
8.4%
c 4407
 
7.5%
a 3821
 
6.5%
2646
 
4.5%
o 2597
 
4.4%
d 2385
 
4.1%
L 2124
 
3.6%
Other values (13) 8264
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13062
22.2%
t 8473
14.4%
l 6064
10.3%
S 4933
 
8.4%
c 4407
 
7.5%
a 3821
 
6.5%
2646
 
4.5%
o 2597
 
4.4%
d 2385
 
4.1%
L 2124
 
3.6%
Other values (13) 8264
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13062
22.2%
t 8473
14.4%
l 6064
10.3%
S 4933
 
8.4%
c 4407
 
7.5%
a 3821
 
6.5%
2646
 
4.5%
o 2597
 
4.4%
d 2385
 
4.1%
L 2124
 
3.6%
Other values (13) 8264
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13062
22.2%
t 8473
14.4%
l 6064
10.3%
S 4933
 
8.4%
c 4407
 
7.5%
a 3821
 
6.5%
2646
 
4.5%
o 2597
 
4.4%
d 2385
 
4.1%
L 2124
 
3.6%
Other values (13) 8264
14.1%

City
Categorical

High correlation  Missing 

Distinct7
Distinct (%)0.1%
Missing1420
Missing (%)15.4%
Memory size585.4 KiB
Mumbai
3222 
Select
2249 
Thane & Outskirts
752 
Other Cities
686 
Other Cities of Maharashtra
457 
Other values (2)
454 

Length

Max length27
Median length6
Mean length9.4702046
Min length6

Characters and Unicode

Total characters74057
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelect
2nd rowSelect
3rd rowMumbai
4th rowMumbai
5th rowMumbai

Common Values

ValueCountFrequency (%)
Mumbai 3222
34.9%
Select 2249
24.3%
Thane & Outskirts 752
 
8.1%
Other Cities 686
 
7.4%
Other Cities of Maharashtra 457
 
4.9%
Other Metro Cities 380
 
4.1%
Tier II Cities 74
 
0.8%
(Missing) 1420
15.4%

Length

2025-07-16T15:25:58.169398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:58.330115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
mumbai 3222
26.2%
select 2249
18.3%
cities 1597
13.0%
other 1523
12.4%
752
 
6.1%
thane 752
 
6.1%
outskirts 752
 
6.1%
of 457
 
3.7%
maharashtra 457
 
3.7%
metro 380
 
3.1%
Other values (2) 148
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 8824
11.9%
t 7710
 
10.4%
i 7242
 
9.8%
a 5802
 
7.8%
4469
 
6.0%
M 4059
 
5.5%
u 3974
 
5.4%
r 3643
 
4.9%
s 3558
 
4.8%
m 3222
 
4.4%
Other values (14) 21554
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74057
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8824
11.9%
t 7710
 
10.4%
i 7242
 
9.8%
a 5802
 
7.8%
4469
 
6.0%
M 4059
 
5.5%
u 3974
 
5.4%
r 3643
 
4.9%
s 3558
 
4.8%
m 3222
 
4.4%
Other values (14) 21554
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74057
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8824
11.9%
t 7710
 
10.4%
i 7242
 
9.8%
a 5802
 
7.8%
4469
 
6.0%
M 4059
 
5.5%
u 3974
 
5.4%
r 3643
 
4.9%
s 3558
 
4.8%
m 3222
 
4.4%
Other values (14) 21554
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74057
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8824
11.9%
t 7710
 
10.4%
i 7242
 
9.8%
a 5802
 
7.8%
4469
 
6.0%
M 4059
 
5.5%
u 3974
 
5.4%
r 3643
 
4.9%
s 3558
 
4.8%
m 3222
 
4.4%
Other values (14) 21554
29.1%

Asymmetrique Activity Index
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing4218
Missing (%)45.6%
Memory size551.8 KiB
02.Medium
3839 
01.High
821 
03.Low
 
362

Length

Max length9
Median length9
Mean length8.4567901
Min length6

Characters and Unicode

Total characters42470
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02.Medium
2nd row02.Medium
3rd row02.Medium
4th row02.Medium
5th row02.Medium

Common Values

ValueCountFrequency (%)
02.Medium 3839
41.5%
01.High 821
 
8.9%
03.Low 362
 
3.9%
(Missing) 4218
45.6%

Length

2025-07-16T15:25:58.524594image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:58.670199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
02.medium 3839
76.4%
01.high 821
 
16.3%
03.low 362
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 5022
11.8%
. 5022
11.8%
i 4660
11.0%
M 3839
9.0%
2 3839
9.0%
e 3839
9.0%
d 3839
9.0%
u 3839
9.0%
m 3839
9.0%
1 821
 
1.9%
Other values (7) 3911
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5022
11.8%
. 5022
11.8%
i 4660
11.0%
M 3839
9.0%
2 3839
9.0%
e 3839
9.0%
d 3839
9.0%
u 3839
9.0%
m 3839
9.0%
1 821
 
1.9%
Other values (7) 3911
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5022
11.8%
. 5022
11.8%
i 4660
11.0%
M 3839
9.0%
2 3839
9.0%
e 3839
9.0%
d 3839
9.0%
u 3839
9.0%
m 3839
9.0%
1 821
 
1.9%
Other values (7) 3911
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5022
11.8%
. 5022
11.8%
i 4660
11.0%
M 3839
9.0%
2 3839
9.0%
e 3839
9.0%
d 3839
9.0%
u 3839
9.0%
m 3839
9.0%
1 821
 
1.9%
Other values (7) 3911
9.2%

Asymmetrique Profile Index
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing4218
Missing (%)45.6%
Memory size550.1 KiB
02.Medium
2788 
01.High
2203 
03.Low
 
31

Length

Max length9
Median length9
Mean length8.1041418
Min length6

Characters and Unicode

Total characters40699
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02.Medium
2nd row02.Medium
3rd row01.High
4th row01.High
5th row01.High

Common Values

ValueCountFrequency (%)
02.Medium 2788
30.2%
01.High 2203
23.8%
03.Low 31
 
0.3%
(Missing) 4218
45.6%

Length

2025-07-16T15:25:58.833768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T15:25:58.970814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
02.medium 2788
55.5%
01.high 2203
43.9%
03.low 31
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 5022
12.3%
. 5022
12.3%
i 4991
12.3%
M 2788
6.9%
2 2788
6.9%
e 2788
6.9%
d 2788
6.9%
u 2788
6.9%
m 2788
6.9%
1 2203
 
5.4%
Other values (7) 6733
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5022
12.3%
. 5022
12.3%
i 4991
12.3%
M 2788
6.9%
2 2788
6.9%
e 2788
6.9%
d 2788
6.9%
u 2788
6.9%
m 2788
6.9%
1 2203
 
5.4%
Other values (7) 6733
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5022
12.3%
. 5022
12.3%
i 4991
12.3%
M 2788
6.9%
2 2788
6.9%
e 2788
6.9%
d 2788
6.9%
u 2788
6.9%
m 2788
6.9%
1 2203
 
5.4%
Other values (7) 6733
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5022
12.3%
. 5022
12.3%
i 4991
12.3%
M 2788
6.9%
2 2788
6.9%
e 2788
6.9%
d 2788
6.9%
u 2788
6.9%
m 2788
6.9%
1 2203
 
5.4%
Other values (7) 6733
16.5%

Asymmetrique Activity Score
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)0.2%
Missing4218
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean14.306252
Minimum7
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2025-07-16T15:25:59.099482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q114
median14
Q315
95-th percentile17
Maximum18
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3866941
Coefficient of variation (CV)0.096929233
Kurtosis1.2330856
Mean14.306252
Median Absolute Deviation (MAD)1
Skewness-0.3833797
Sum71846
Variance1.9229205
MonotonicityNot monotonic
2025-07-16T15:25:59.242197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
14 1771
19.2%
15 1293
 
14.0%
13 775
 
8.4%
16 467
 
5.1%
17 349
 
3.8%
12 196
 
2.1%
11 95
 
1.0%
10 57
 
0.6%
9 9
 
0.1%
18 5
 
0.1%
Other values (2) 5
 
0.1%
(Missing) 4218
45.6%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 4
 
< 0.1%
9 9
 
0.1%
10 57
 
0.6%
11 95
 
1.0%
12 196
 
2.1%
13 775
8.4%
14 1771
19.2%
15 1293
14.0%
16 467
 
5.1%
ValueCountFrequency (%)
18 5
 
0.1%
17 349
 
3.8%
16 467
 
5.1%
15 1293
14.0%
14 1771
19.2%
13 775
8.4%
12 196
 
2.1%
11 95
 
1.0%
10 57
 
0.6%
9 9
 
0.1%

Asymmetrique Profile Score
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)0.2%
Missing4218
Missing (%)45.6%
Infinite0
Infinite (%)0.0%
Mean16.344883
Minimum11
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size72.3 KiB
2025-07-16T15:25:59.400218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14
Q115
median16
Q318
95-th percentile20
Maximum20
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.811395
Coefficient of variation (CV)0.11082337
Kurtosis-0.61973145
Mean16.344883
Median Absolute Deviation (MAD)1
Skewness0.22173872
Sum82084
Variance3.2811519
MonotonicityNot monotonic
2025-07-16T15:25:59.534385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
15 1759
19.0%
18 1071
 
11.6%
16 599
 
6.5%
17 579
 
6.3%
20 308
 
3.3%
19 245
 
2.7%
14 226
 
2.4%
13 204
 
2.2%
12 22
 
0.2%
11 9
 
0.1%
(Missing) 4218
45.6%
ValueCountFrequency (%)
11 9
 
0.1%
12 22
 
0.2%
13 204
 
2.2%
14 226
 
2.4%
15 1759
19.0%
16 599
 
6.5%
17 579
 
6.3%
18 1071
11.6%
19 245
 
2.7%
20 308
 
3.3%
ValueCountFrequency (%)
20 308
 
3.3%
19 245
 
2.7%
18 1071
11.6%
17 579
 
6.3%
16 599
 
6.5%
15 1759
19.0%
14 226
 
2.4%
13 204
 
2.2%
12 22
 
0.2%
11 9
 
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
9240 
ValueCountFrequency (%)
False 9240
100.0%
2025-07-16T15:25:59.648262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

A free copy of Mastering The Interview
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
False
6352 
True
2888 
ValueCountFrequency (%)
False 6352
68.7%
True 2888
31.3%
2025-07-16T15:25:59.748527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Last Notable Activity
Categorical

High correlation 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size607.6 KiB
Modified
3407 
Email Opened
2827 
SMS Sent
2172 
Page Visited on Website
 
318
Olark Chat Conversation
 
183
Other values (11)
 
333

Length

Max length28
Median length8
Mean length10.320996
Min length8

Characters and Unicode

Total characters95366
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowModified
2nd rowEmail Opened
3rd rowEmail Opened
4th rowModified
5th rowModified

Common Values

ValueCountFrequency (%)
Modified 3407
36.9%
Email Opened 2827
30.6%
SMS Sent 2172
23.5%
Page Visited on Website 318
 
3.4%
Olark Chat Conversation 183
 
2.0%
Email Link Clicked 173
 
1.9%
Email Bounced 60
 
0.6%
Unsubscribed 47
 
0.5%
Unreachable 32
 
0.3%
Had a Phone Conversation 14
 
0.2%
Other values (6) 7
 
0.1%

Length

2025-07-16T15:25:59.922120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
modified 3407
21.3%
email 3063
19.1%
opened 2827
17.6%
sms 2172
13.6%
sent 2172
13.6%
website 319
 
2.0%
on 319
 
2.0%
visited 318
 
2.0%
page 318
 
2.0%
conversation 197
 
1.2%
Other values (23) 910
 
5.7%

Most occurring characters

ValueCountFrequency (%)
e 13075
13.7%
i 11430
12.0%
d 10260
10.8%
6782
 
7.1%
S 6519
 
6.8%
n 6041
 
6.3%
M 5581
 
5.9%
o 4199
 
4.4%
a 4042
 
4.2%
l 3454
 
3.6%
Other values (27) 23983
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 13075
13.7%
i 11430
12.0%
d 10260
10.8%
6782
 
7.1%
S 6519
 
6.8%
n 6041
 
6.3%
M 5581
 
5.9%
o 4199
 
4.4%
a 4042
 
4.2%
l 3454
 
3.6%
Other values (27) 23983
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 13075
13.7%
i 11430
12.0%
d 10260
10.8%
6782
 
7.1%
S 6519
 
6.8%
n 6041
 
6.3%
M 5581
 
5.9%
o 4199
 
4.4%
a 4042
 
4.2%
l 3454
 
3.6%
Other values (27) 23983
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 13075
13.7%
i 11430
12.0%
d 10260
10.8%
6782
 
7.1%
S 6519
 
6.8%
n 6041
 
6.3%
M 5581
 
5.9%
o 4199
 
4.4%
a 4042
 
4.2%
l 3454
 
3.6%
Other values (27) 23983
25.1%

Interactions

2025-07-16T15:25:47.648282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:43.738637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.483087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.174573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.879416image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.736390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:47.814408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:43.876276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.614776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.292606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.022461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.899653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:47.964664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:43.996325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.721262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.403661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.156882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:47.050460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:48.111428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.114402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.830182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.510878image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.291480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:47.179508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:48.257875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.250124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.931427image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.629526image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.438221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:47.313236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:48.406822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:44.366483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.070276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:45.747595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:46.591602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-16T15:25:47.473345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-16T15:26:00.098652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A free copy of Mastering The InterviewAsymmetrique Activity IndexAsymmetrique Activity ScoreAsymmetrique Profile IndexAsymmetrique Profile ScoreCityConvertedCountryDigital AdvertisementDo Not CallDo Not EmailHow did you hear about X EducationLast ActivityLast Notable ActivityLead NumberLead OriginLead ProfileLead QualityLead SourceNewspaperNewspaper ArticlePage Views Per VisitSearchSpecializationTagsThrough RecommendationsTotal Time Spent on WebsiteTotalVisitsWhat is your current occupationWhat matters most to you in choosing a courseX Education Forums
A free copy of Mastering The Interview1.0000.0960.1480.2590.3730.5040.0380.0910.0000.0000.0540.3090.1970.1100.1160.5690.0580.1400.6690.0000.0000.1270.0000.4580.1800.0040.2070.0160.0230.0000.000
Asymmetrique Activity Index0.0961.0000.9990.1440.1810.0460.1900.0000.0000.0000.0680.0460.3150.1700.1020.2040.1810.1730.2881.0000.0000.0380.0060.0340.4980.0000.1290.0280.0660.0341.000
Asymmetrique Activity Score0.1480.9991.0000.169-0.1320.0500.4190.0000.0000.0000.1280.0430.2380.096-0.0750.1790.1340.1940.1741.0000.000-0.2580.0000.0380.3150.000-0.168-0.2000.0620.0711.000
Asymmetrique Profile Index0.2590.1440.1691.0000.9990.4490.1730.0000.0110.0000.0000.2220.1900.0980.0770.4800.2300.1430.3821.0000.0000.0380.0060.4910.1630.0000.1530.0000.1200.0001.000
Asymmetrique Profile Score0.3730.181-0.1320.9991.0000.4390.2780.0000.0000.0000.0660.1180.1110.056-0.1600.4140.3490.2060.3081.0000.0000.2470.0350.3070.1230.0000.2260.2340.1260.0001.000
City0.5040.0460.0500.4490.4391.0000.0720.0660.0190.0000.0780.1870.1170.0650.0280.3960.0900.1220.2480.0000.0230.0650.0130.3370.0760.0470.1190.0000.0200.0000.042
Converted0.0380.1900.4190.1730.2780.0721.0000.0070.0000.0040.1350.0510.3960.3800.0870.3250.3790.6590.3360.0000.0000.0000.0000.0630.9310.0100.4260.0010.3020.0000.000
Country0.0910.0000.0000.0000.0000.0660.0071.0000.0000.0000.0930.0000.0570.0740.0250.0000.0220.0290.1120.0000.0000.2140.0000.0190.0330.0000.0000.0000.0000.0000.000
Digital Advertisement0.0000.0000.0000.0110.0000.0190.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1760.0000.0650.0100.0760.0930.0000.0000.0000.0000.250
Do Not Call0.0000.0000.0000.0000.0000.0000.0040.0000.0001.0000.0000.0430.0000.0000.0080.0140.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.000
Do Not Email0.0540.0680.1280.0000.0660.0780.1350.0930.0000.0001.0000.0540.6960.4160.0990.1010.0900.1860.1390.0000.0000.0450.0000.1300.2780.0000.0630.0650.0480.0000.000
How did you hear about X Education0.3090.0460.0430.2220.1180.1870.0510.0000.0000.0430.0541.0000.0730.0410.0430.2290.0360.0500.2080.0000.0000.0000.0000.1280.0460.0000.0700.0750.0000.0000.000
Last Activity0.1970.3150.2380.1900.1110.1170.3960.0570.0000.0000.6960.0731.0000.6860.0840.2010.1250.2800.1240.0000.0000.0410.0000.0770.1620.0240.0890.0170.0760.0000.000
Last Notable Activity0.1100.1700.0960.0980.0560.0650.3800.0740.0000.0000.4160.0410.6861.0000.0870.1030.0980.2300.0660.0000.0000.0000.0000.0390.1640.0580.0610.0310.0600.0000.000
Lead Number0.1160.102-0.0750.077-0.1600.0280.0870.0250.0000.0080.0990.0430.0840.0871.0000.0870.1410.1120.1270.0110.0000.0660.0230.0350.0890.0000.0310.0570.0590.0000.000
Lead Origin0.5690.2040.1790.4800.4140.3960.3250.0000.0000.0140.1010.2290.2010.1030.0871.0000.1110.1980.9020.0000.0000.0830.0010.3600.2770.0000.2100.0000.0860.0000.000
Lead Profile0.0580.1810.1340.2300.3490.0900.3790.0220.0000.0000.0900.0360.1250.0980.1410.1111.0000.4170.1410.0000.0000.0160.0000.1350.3710.0000.0700.0000.1950.0001.000
Lead Quality0.1400.1730.1940.1430.2060.1220.6590.0290.0000.0000.1860.0500.2800.2300.1120.1980.4171.0000.1870.0000.0000.0060.0000.0910.5420.0240.1400.0000.2130.0361.000
Lead Source0.6690.2880.1740.3820.3080.2480.3360.1120.0000.0000.1390.2080.1240.0660.1270.9020.1410.1871.0000.0000.0000.1370.0000.1360.1460.0000.1730.0140.1030.0000.000
Newspaper0.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.000
Newspaper Article0.0000.0000.0000.0000.0000.0230.0000.0000.1760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0160.0930.0290.0000.1330.0250.0000.0000.0000.353
Page Views Per Visit0.1270.038-0.2580.0380.2470.0650.0000.2140.0000.0000.0450.0000.0410.0000.0660.0830.0160.0060.1370.0000.0161.0000.0130.0750.0410.0140.5720.8500.0150.0000.000
Search0.0000.0060.0000.0060.0350.0130.0000.0000.0650.0000.0000.0000.0000.0000.0230.0010.0000.0000.0000.0000.0930.0131.0000.0290.0000.2520.0180.0000.0000.0000.133
Specialization0.4580.0340.0380.4910.3070.3370.0630.0190.0100.0340.1300.1280.0770.0390.0350.3600.1350.0910.1360.0000.0290.0750.0291.0000.0660.0000.0890.0470.1000.0000.062
Tags0.1800.4980.3150.1630.1230.0760.9310.0330.0760.0000.2780.0460.1620.1640.0890.2770.3710.5420.1460.0000.0000.0410.0000.0661.0000.0000.1340.0000.2070.0491.000
Through Recommendations0.0040.0000.0000.0000.0000.0470.0100.0000.0930.0000.0000.0000.0240.0580.0000.0000.0000.0240.0000.0000.1330.0140.2520.0000.0001.0000.0410.0000.0270.0000.189
Total Time Spent on Website0.2070.129-0.1680.1530.2260.1190.4260.0000.0000.0000.0630.0700.0890.0610.0310.2100.0700.1400.1730.0480.0250.5720.0180.0890.1340.0411.0000.5860.0590.0000.033
TotalVisits0.0160.028-0.2000.0000.2340.0000.0010.0000.0000.0000.0650.0750.0170.0310.0570.0000.0000.0000.0140.0000.0000.8500.0000.0470.0000.0000.5861.0000.0000.0000.000
What is your current occupation0.0230.0660.0620.1200.1260.0200.3020.0000.0000.0000.0480.0000.0760.0600.0590.0860.1950.2130.1030.0000.0000.0150.0000.1000.2070.0270.0590.0001.0000.0001.000
What matters most to you in choosing a course0.0000.0340.0710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0001.0001.000
X Education Forums0.0001.0001.0001.0001.0000.0420.0000.0000.2500.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.3530.0000.1330.0621.0000.1890.0330.0001.0001.0001.000

Missing values

2025-07-16T15:25:48.716374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-16T15:25:49.459548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-16T15:25:50.254401image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Prospect IDLead NumberLead OriginLead SourceDo Not EmailDo Not CallConvertedTotalVisitsTotal Time Spent on WebsitePage Views Per VisitLast ActivityCountrySpecializationHow did you hear about X EducationWhat is your current occupationWhat matters most to you in choosing a courseSearchMagazineNewspaper ArticleX Education ForumsNewspaperDigital AdvertisementThrough RecommendationsReceive More Updates About Our CoursesTagsLead QualityUpdate me on Supply Chain ContentGet updates on DM ContentLead ProfileCityAsymmetrique Activity IndexAsymmetrique Profile IndexAsymmetrique Activity ScoreAsymmetrique Profile ScoreI agree to pay the amount through chequeA free copy of Mastering The InterviewLast Notable Activity
07927b2df-8bba-4d29-b9a2-b6e0beafe620660737APIOlark ChatNoNo00.000.0Page Visited on WebsiteNaNSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoInterested in other coursesLow in RelevanceNoNoSelectSelect02.Medium02.Medium15.015.0NoNoModified
12a272436-5132-4136-86fa-dcc88c88f482660728APIOrganic SearchNoNo05.06742.5Email OpenedIndiaSelectSelectUnemployedBetter Career ProspectsNoNoNoNoNoNoNoNoRingingNaNNoNoSelectSelect02.Medium02.Medium15.015.0NoNoEmail Opened
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